Time varying loudness prediction system

ABSTRACT

Disclosed are methods and systems for predicting time varying loudness in a geographic region. Training data, including noise information, weather information, and traffic information is collected from a plurality of sensors located in a plurality of geographic regions. The information is collected during multiple time periods. The noise information includes time varying loudness. Static features of the geographic regions are also defined and included in the training data. The static and time varying dynamic features train a model. The model is used predict time varying loudness within a different region and at a time later than times the training data is collected. The predicted loudness levels are utilized, in some aspects, to determine a route for an aircraft.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Application No.62/859,685, filed Jun. 10, 2019 and entitled “Time Varying LoudnessPrediction via a Trained Model.” The contents of this prior applicationis considered part of this application, and is hereby incorporated byreference in its entirety.

FIELD

This document pertains generally, but not by way of limitation, tomethods and systems for predicting noise and generating noise map datafor a particular environment.

BACKGROUND

Every day, millions of hours are wasted on the road worldwide. Theaverage San Francisco resident spends 230 hours per year commutingbetween work and home, which equates to more half a million hours ofproductivity lost every single day in this city alone. In Los Angelesand Sydney, residents spend seven whole working weeks each yearcommuting, two of which are wasted unproductively stuck in gridlock. Inmany global megacities, the problem is more severe: the average commutein Mumbai exceeds a staggering 90 minutes. For workers, this amounts toless time with family, less time at work growing our economies, moremoney spent on fuel—and a marked increase in worker stress levels. Astudy in the American Journal of Preventative Medicine, for example,found that those who commute more than 10 miles were at increased oddsof elevated blood pressure.

One proposed solution to the above transportation and mobility problemsmay lie with on-demand aviation. On-demand aviation has the potential toradically improve urban mobility, giving people back time lost in theirdaily commutes. Urban air transportation provides the possibility to usethree-dimensional airspace to alleviate transportation congestion on theground. A network of small, electric aircraft that take off and landvertically (called VTOL aircraft for Vertical Take-off and Landing, andpronounced vee-tol), would enable rapid, reliable transportation betweensuburbs and cities and, ultimately, within cities.

While on-demand aviation provides solutions to the above problems, onefactor inhibiting adoption is a concern for noise generated by theseoperations. Exposure to noise has both psychological and physiologicalimpacts on those within range of that noise. For example, one reasonheliports are not currently located in or near large demand centers isthat the noise from these rotorcraft would be too disruptive andunacceptable to the community.

Thus, improved methods of operating on-demand aircraft in a manner thatminimizing noise impact to a surrounding community are needed.

DRAWINGS

In the drawings, which are not necessarily drawn to scale, like numeralsmay describe similar components in different views. Like numerals havingdifferent letter suffixes may represent different instances of similarcomponents. Some embodiments are illustrated by way of example, and notof limitation, in the figures of the accompanying drawings.

FIG. 1 illustrates a transportation environment 100 in accordance withone embodiment.

FIG. 2 is a diagrammatic representation of an autonomous vehicle system,in accordance with some example embodiments.

FIG. 3 is an overview diagram of data flow in one or more of thedisclosed embodiments.

FIGS. 4A and 4B show noise measurement locations in a metropolitan area.

FIG. 5 shows two examples of noise sensors used to collect noisemeasurements in at least some of the disclosed embodiments.

FIG. 6A. shows observations collected via the measurement devicesdescribed above.

FIG. 6B shows noise levels in a geographic region at a particular timeof day.

FIG. 7A shows node optimization based on loudness, according to at leastsome of the disclosed embodiments.

FIG. 7B shows selection of a sky lane based on loudness data, accordingto at least some of the disclosed embodiments.

FIG. 8 shows an example machine learning module 800 according to someexamples of the present disclosure

FIG. 9 is a flowchart of a process for normalizing training dataaccording to at least some of the disclosed embodiments.

FIG. 10 shows one observation of analysis of the training set describedabove.

FIG. 11 shows a correlation matrix of one embodiment's model features.

FIG. 12A is a flowchart of a process for predicting time varyingloudness over multiple frequencies.

FIG. 12B is a continuation of the flowchart of FIG. 12A.

FIG. 13 is a block diagram showing one example of a softwarearchitecture for a computing device.

FIG. 14 is a block diagram illustrating a computing device hardwarearchitecture.

DETAILED DESCRIPTION

The following description and the drawings sufficiently illustratespecific embodiments to enable those skilled in the art to practicethem. Other embodiments may incorporate structural, logical, electrical,process, and other changes. Portions and features of some embodimentsmay be included in, or substituted for, those of other embodiments.Embodiments set forth in the claims encompass all available equivalentsof those claims.

There are a variety of reasons to understand a noise profile of ageographic area. For example, a noise profile or noise level candetermine a tolerance for additional noise to be introduced into theenvironment. Some government regulations require that proposed activityadd no more than a defined percentage to an existing noise level. Thus,relatively quiet regions may be more difficult to operate within withoutviolating these governmental requirements.

When planning operations, it can be challenging to understand a currentnoise environment. This may be especially true when planning aircraftoperations, which cover large distances and have the potential to impactlarge geographic areas. Noise information for these vast areas isgenerally not available, making planning operations in these regionschallenging. Thus, a technical problem is presented in that this lack ofunderstanding of the noise environments in which an aircraft will beoperating results. This technical problem results in assignment ofaircraft routes which may adversely effect ground activities more thannecessary. This can result in a need to reduce a level of flightactivity below which might be supportable if the noise environment wasbetter understood.

The disclosed embodiments solve this technical problem by providing amodel that is able to predict time varying loudness levels acrossmultiple frequencies for almost any geographic region for which a set offeature parameters are understood. To accomplish this, static anddynamic feature information for a plurality of regions is collected.Static feature information includes information such as elevation,percentage of vegetation in the region, distance from one or more roads,and other features of a geographic region that do not generally changeover time, or at least change at a relatively slow rate. Dynamic featureinformation is also collected via deployment of a variety of sensors.Some sensors are configured to collect weather data in a region,including, for example, one or more of temperature, dewpoint, humidity,wind speed, wind direction, pressure, or other weather data. Othersensors collect noise information within the region. For example, noisesensors are configured to collect time varying loudness data on aplurality of frequencies. Other sensors record traffic amount and volumeinformation. Traffic in this context includes, in various embodiments,but is not limited to, one or more of road traffic (bus, car,motorcycle, scooter, or other traffic), air traffic, and/or railtraffic. Each of the weather, noise, and traffic data is correlated witha time period in which the data is collected.

The static and dynamic feature data for each of a plurality of regionsare used to train a model. A percentage of the feature data is withheldfrom the training set and then used to test accuracy levels of the modelat predicting the noise data for a region. The results of these testsare described further below. Thus, the disclosed embodiments provide forprediction of loudness information for a geographic region over aplurality of time periods. Notably, the geographic region for whichpredictions are made does not necessarily need to be included in thetraining data. Further, predictions for time periods in the future, orat least after a most recent training data is collected, may also beprovided by the disclosed embodiments. The data produced from the modelcan be used to generate noise map data for a particular geographic area.This can include, for example, the geographic area associated with thedata input into the model or another geographic area (for which themodel can predict loudness). Moreover, these predictions may be used forrouting of aircraft through a geographic region (e.g., based on thenoise map data). For example, noise predictions for the geographicregion may be used to determine whether to route an aircraft through theregion, at what altitude to route the aircraft, and/or at what times.For example, if a region has a relatively high level of predictedbackground loudness (e.g. background loudness above a predefinedthreshold), some embodiments route an aircraft at a lower altitude overthe region than if the region has a relatively lower level of backgroundloudness. (e.g. predicted background loudness lower than the predefinedthreshold or lower than a second predefined threshold). Some embodimentsdetermine a minimum predicted background noise loudness along anaircraft route, and set an aircraft's altitude over the route based onthe minimum. For example, if the minimum predicted background noiseloudness along the route is below a predefined noise threshold, someembodiments set the aircraft's altitude along the route to be above apredefined altitude threshold (e.g. to minimize noise impact onrelatively quiet areas). Urban planning may also be influenced by themodel's predictions. For example, locations of and or hours of operationof airports and/or sky ports may be influenced based on predictionsprovided by the disclosed model.

A-weighted sound pressure level (L_(A)) alone may be inaccurate, in somecases, in estimating human perception of noise. L_(A) was developed togauge whether intentionally generated sounds, such as those made by atelephone was loud enough to achieve their intended purpose. However,L_(A) does not measure whether a sound is bothersome to a human.Instead, loudness provides a better metric for tracking the subjectiveperception of sound pressure.

By examining loudness, instead of L_(A), the disclosed embodiments areable to identify and account for certain types of acoustic nuance. Forexample, the disclosed embodiments provide some level-dependence offrequency weighting. Auditory reception behaves differently at differentsound levels. Sound pressure levels are traditionally weighted along acurve in order to account for what humans can hear. Noises outside ofour audible frequency range (20 Hz-20 kHz) are minimized or discountedby this weighting. A-weighting is designed to mimic human sensitivity tosingle tones at the level of soft human speech. Other weighting curveshave been proposed for higher sound levels (at which humans are moresensitive to low and high frequency sound) but modern practice is tocontinuously adjust the weighting curve depending on level rather thanuse discrete steps.

Some of the disclosed embodiments further provide for spectral loudnesssummation. For example, two separate sounds can share the same level,but differences in spectrum will contribute to differences in loudness.Besides sensitivity variations with frequency, the ear is less sensitiveto sounds that are close in pitch to stronger sounds, and is moresensitive to sounds some distance away in pitch.

Some embodiments consider a temporal shape of sound. In particular, someseparate sounds share an equivalent level and spectrum, but can still beperceived very differently. Temporal shape contributes to the audibilityof the sound, since humans have different integration times for onsetand decay of sound events. Even if a particular sound is severaldecibels less in sound pressure than background noise, it can stillstand out depending on how rapidly it changes.

Loudness can vary depending on a length of time for which it isrecorded. For understanding how aircraft can blend into urbansoundscapes, time-varying loudness (TVL) is of interest. TVL includesboth short-term (rapid onset) and long-term (memory) reception in theauditory system, accounting for both the inertia of our physiologicalsystem (e.g., human neurons do not fire instantaneously), and theaudibility of “pulses” like rotor blades. Even at lower dB(A) levels,helicopter sounds (for instance) are still audible and stand out fromthe urban soundscape. Averaging over longer durations (50 ms+) mayobfuscate the audibility of pulse-type sounds, and make it moredifficult to confidently ascertain if newly introduced noise blends intothe ambient soundscape.

In order to measure a loudness increase contributed by an on-demandaircraft flight, a degree to which ambient sound masks the sound of theaircraft is measured. Ambient TVL versus time is considered for aplurality of spectral regions that will experience aircraft noise. Someembodiments analyze approximately thirty bands of frequencies. Someembodiments report an average loudness measurement over the entirefrequency range. Data is generally collected, in at least some of theembodiments, within each frequency band.

All this is to say that simply relying on the average physicaldisplacement of air as a proxy for noise would be an irresponsiblyoversimplified success metric for our operation. A system would need toaccount for these nuances in measuring vehicular noise and map thatagainst the same nuances in our urban soundscape.

Thus, the disclosed embodiments simultaneously inform the relative totalloudness in a given location and the partial loudness due to theoperation of flights. Of the several loudness metrics in common use,time-varying loudness is most informative for the applicationscontemplated by the disclosed embodiments. Specifically, time-varyingloudness TVL given equivalent rectangular band i, location l and hour ofday h should accurately capture the nuances of our distributed urbansoundscape:

TVL(i,l,h)

For any given location and hour, TVL is integrated, in some embodiments,into equivalent loudness for that hour. As additional aircraftoperations and flight routes are performed within a region, a TVL atthese times and locations will change based on the perceived loudness ofair operations relative to an existing soundscape. The disclosedembodiments capture this by measuring the delta in TVL between ambientsound and ambient sound after aircraft operations are introduced. Someembodiments perform these determinations for each spectral region.

Some embodiments also include a weighting factor to account forpopulation density in a particular region and at a particular time ofday, as below:

weighted impact(l,h)=ΔTVL(l,h)*P(l,h)

Thus, the disclosed embodiments provide for establishing a baseline forTVL, which permits the calculation of total loudness with operationsadded. So TVL(i,l,h) will serve as our primary soundscape metric.

FIG. 1 is a schematic representation of a transportation environment100, according to an example embodiment, in which an aerial vehiclefleet 102 and a ground vehicle fleet 104 provide mobility services(e.g., people transport, goods delivery).

Interactions, communications, and coordination of movement betweenvehicles of the aerial vehicle fleet 102 (e.g., vertical take-off andlanding (VTOL) vehicles 115, helicopters 124 and aircraft 126) and theground vehicle fleet 104 (e.g., automobiles 112, scooters 114, bicycles116, motorbikes 118, and busses 120) are facilitated through one or moremobility network servers 110 that are coupled via various networks 106both the aerial vehicle fleet 102 and ground vehicle fleet 104.Specifically, the mobility network servers 110 may coordinate theacquisition of data from the various sensors of vehicles of the aerialvehicle fleet 102 in order to control the routing and operations ofvarious vehicles of the ground vehicle fleet 104. The mobility networkservers 110 likewise coordinate the acquisition of data from varioussensors are vehicles of the ground vehicle fleet 104 in order to controlthe routing and operations of vehicles of the aerial vehicle fleet 102.For example, the mobility network servers 110 may control theacquisition of traffic monitoring data, by the aerial vehicle fleet 102,in order to assist in routing of vehicles of the ground vehicle fleet104 by a routing engine that forms part of the mobility network servers110.

The mobility network servers 110 is also coupled to an unmanned aerialvehicle traffic management system (UTM) 108, which operates to provideaircraft traffic management and de-confliction services to the aerialvehicle fleet 102. In one embodiment, the UTM 108 provides airspacemanagement that various altitudes up to several thousand feet. The UTM108 may also provide high-volume voiceless air traffic controlinteractions, and also integrates with air traffic control systemsassociated with airports. The mobility network servers 110 useconnections provided by the UTM 108 to communicate with the vehicles ofthe aerial vehicle fleet 102, passing along routing and otherinformation.

The acquisition of data for traffic monitoring, for example, by theaerial vehicle fleet 102 may be based on the aerial vehicles fly tripspredetermined and/or optimized by an UTM network. Connection to the UTM108 is facilitated across multiple communications frequencies as neededfor operation. Onboard telemetry of vehicles in the aerial vehicle fleet102 is supplemented with GPS data in addition to other communicationstreams (GPS, 5G, etc.).

The UTM 108 is connected to the mobility network servers 110 thatmanage/oversee the operations of ground vehicles. The UTM 108 furthercommunicates with third-party UAS Service Suppliers (USS) andsupplemental data service providers (SDSPs) to facilitate the transferof data to these third-party services.

The mobility network servers 110 further perform predetermination and/oroptimization of trips of the aerial vehicle fleet 102 are based, forexample, on the payload to be transported from a departing location toan arrival location (e.g., vertiport, fiducial, ad-hoc location). Forexample, flights by the aerial vehicle fleet 102 may be optimized toincrease overall throughput, and thus efficiency of the system. Aircraft126 and vertical take-off and landing (VTOL) vehicles such as a VTOLvehicle 115 and/or a helicopter 124 may fly within dynamically allocatedroutes and sky lanes which enable safe, dense operation at scale.Allocation of these routes/sky lanes is determined by the mobilitynetwork servers 110 based on environmental acceptance (e.g., noise),weather, airspace deconfliction, and operational relevancy.

The aerial vehicles of the aerial vehicle fleet 102 can include humancontrolled aerial vehicles. For instance, a vehicle operator (e.g.,pilot) can be located within the aerial vehicle to control the aerialvehicle. In some implementations, the vehicle operator can be located ata remote location and utilize a computing device presenting a userinterface to remotely control the aerial vehicle.

In some implementations, the aerial vehicle fleet 102 can includeautonomous aerial vehicles. FIG. 2 depicts a block diagram of an aerialvehicle 200, according to example aspects of the present disclosure. Theaerial vehicle 200 can be, for example, be an autonomous orsemi-autonomous aerial vehicle. The aerial vehicle 200 includes one ormore sensors 218, an aerial vehicle autonomy system 212, and one or morevehicle control systems 228.

The aerial vehicle autonomy system 212 can be engaged to control theaerial vehicle 200 or to assist in controlling the aerial vehicle 200.In particular, the aerial vehicle autonomy system 212 receives sensordata from the sensors 218, attempts to comprehend the environmentsurrounding the aerial vehicle 200 by performing various processingtechniques on data collected by the sensors 218 and generates anappropriate motion path through an environment. The aerial vehicleautonomy system 212 can control the one or more vehicle control systems228 to operate the aerial vehicle 200 according to the motion path.

The aerial vehicle autonomy system 212 includes a perception system 220,a prediction system 224, a motion planning system 226, and a pose system222 that cooperate to perceive the surrounding environment of the aerialvehicle 200 and determine a motion plan for controlling the motion ofthe aerial vehicle 200 accordingly.

Various portions of the aerial vehicle autonomy system 212 receivesensor data from the sensors 218. For example, the sensors 218 mayinclude remote-detection sensors as well as motion sensors such as aninertial measurement unit (IMU), one or more encoders, or the like. Thesensor data can include information that describes the location ofobjects within the surrounding environment of the aerial vehicle 200,information that describes the motion of the vehicle, and so forth.

The sensors 218 may also include one or more remote-detection sensors orsensor systems, such as a LIDAR, a RADAR, one or more cameras, etc. Asone example, a LIDAR system of the sensors 218 generates sensor data(e.g., remote-detection sensor data) that includes the location (e.g.,in three-dimensional space relative to the LIDAR system) of a number ofpoints that correspond to objects that have reflected a ranging laser.For example, the LIDAR system can measure distances by measuring theTime of Flight (TOF) that it takes a short laser pulse to travel fromthe sensor to an object and back, calculating the distance from theknown speed of light.

As another example, for a RADAR system of the sensors 218 generatessensor data (e.g., remote-detection sensor data) that includes thelocation (e.g., in three-dimensional space relative to the RADAR system)of a number of points that correspond to objects that have reflectedranging radio waves. For example, radio waves (e.g., pulsed orcontinuous) transmitted by the RADAR system can reflect off an objectand return to a receiver of the RADAR system, giving information aboutthe object's location and speed. Thus, a RADAR system can provide usefulinformation about the current speed of an object.

As yet another example, one or more cameras of the sensors 218 maygenerate sensor data (e.g., remote sensor data) including still ormoving images. Various processing techniques (e.g., range imagingtechniques such as, for example, structure from motion, structuredlight, stereo triangulation, and/or other techniques) can be performedto identify the location (e.g., in three-dimensional space relative tothe one or more cameras) of a number of points that correspond toobjects that are depicted in image or images captured by the one or morecameras. Other sensor systems can identify the location of points thatcorrespond to objects as well.

As another example, the sensors 218 can include a positioning system.The positioning system can determine a current position of the aerialvehicle 200. The positioning system can be any device or circuitry foranalyzing the position of the aerial vehicle 200. For example, thepositioning system can determine a position by using one or more ofinertial sensors, a satellite positioning system such as a GlobalPositioning System (GPS), based on IP address, by using triangulationand/or proximity to network access points or other network components(e.g., cellular towers, WiFi access points, etc.) and/or other suitabletechniques. The position of the aerial vehicle 200 can be used byvarious systems of the aerial vehicle autonomy system 212.

Thus, the sensors 218 can be used to collect sensor data that includesinformation that describes the location (e.g., in three-dimensionalspace relative to the aerial vehicle 200) of points that correspond toobjects within the surrounding environment of the aerial vehicle 200. Insome implementations, the sensors 218 can be located at variousdifferent locations on the aerial vehicle 200. As an example, one ormore cameras, RADAR and/or LIDAR sensors.

The pose system 222 receives some or all of the sensor data from thesensors 218 and generates vehicle poses for the aerial vehicle 200. Avehicle pose describes the position (including altitude) and attitude ofthe vehicle. The position of the aerial vehicle 200 is a point in athree-dimensional space. In some examples, the position is described byvalues for a set of Cartesian coordinates, although any other suitablecoordinate system may be used. The attitude of the aerial vehicle 200generally describes the way in which the aerial vehicle 200 is orientedat its position. In some examples, attitude is described by a yaw aboutthe vertical axis, a pitch about a first horizontal axis and a rollabout a second horizontal axis. In some examples, the pose system 222generates vehicle poses periodically (e.g., every second, every halfsecond, etc.) The pose system 222 appends time stamps to vehicle poses,where the time stamp for a pose indicates the point in time that isdescribed by the pose. The pose system 222 generates vehicle poses bycomparing sensor data (e.g., remote sensor data) to map data 216describing the surrounding environment of the aerial vehicle 200.

In some examples, the pose system 222 includes localizers and a posefilter. Localizers generate pose estimates by comparing remote sensordata (e.g., LIDAR, RADAR, etc.) to map data. The pose filter receivespose estimates from the one or more localizers as well as other sensordata such as, for example, motion sensor data from an IMU, encoder,odometer, etc. In some examples, the pose filter executes a Kalmanfilter or other machine learning algorithm to combine pose estimatesfrom the one or more localizers with motion sensor data to generatevehicle poses. In some examples, localizers generate pose estimates at afrequency less than the frequency at which the pose system 222 generatesvehicle poses. Accordingly, the pose filter generates some vehicle posesby extrapolating from a previous pose estimates.

The perception system 220 detects objects in the surrounding environmentof the aerial vehicle 200 based on the sensor data, the map data 216and/or vehicle poses provided by the pose system 222. The map data 216,for example, may provide detailed information about the surroundingenvironment of the aerial vehicle 200. The map data 216 can provideinformation regarding: the route(s) and/or skylane(s) that the aerialvehicle 200 is to traverse, route(s) and/or skylanes; the position ofother aerial vehicles; the location, attitude, orientation, and/or otherparameters of vertiports or landing zones, weather data (e.g., weatherradar data), noise map data and/or any other map data that providesinformation that assists the aerial vehicle autonomy system 212 incomprehending and perceiving its surrounding environment and itsrelationship thereto. The prediction system 224 uses vehicle posesprovided by the pose system 222 to place aerial vehicle 200 environment.

In some examples, the perception system 220 determines state data forobjects in the surrounding environment of the aerial vehicle 200. Statedata may describe a current state of an object (also referred to asfeatures of the object). The state data for each object describes, forexample, an estimate of the object's: current location (also referred toas position); current speed (also referred to as velocity); currentacceleration; current heading; current orientation; size/shape/footprint(e.g., as represented by a bounding shape such as a bounding polygon orpolyhedron); type/class (e.g., vehicle versus pedestrian versus bicycleversus other); yaw rate; distance from the aerial vehicle 200; minimumpath to interaction with the aerial vehicle 200; minimum time durationto interaction with the aerial vehicle 200; and/or other stateinformation.

In some implementations, the perception system 220 can determine statedata for each object over a number of iterations. In particular, theperception system 220 can update the state data for each object at eachiteration. Thus, the perception system 220 can detect and track objects,such as vehicles, that are proximate to the aerial vehicle 200 overtime.

The prediction system 224 is configured to predict future positions foran object or objects in the environment surrounding the aerial vehicle200 (e.g., an object or objects detected by the perception system 220).The prediction system 224 can generate prediction data associated withobjects detected by the perception system 220. In some examples, theprediction system 224 generates prediction data describing each of therespective objects detected by the perception system 220.

Prediction data for an object can be indicative of one or more predictedfuture locations of the object. For example, the prediction system 224may predict where the object will be located within the next 5 seconds,20 seconds, 200 seconds, etc. Prediction data for an object may indicatea predicted trajectory (e.g., predicted path) for the object within thesurrounding environment of the aerial vehicle 200. For example, thepredicted trajectory (e.g., path) can indicate a path along which therespective object is predicted to travel over time (and/or the speed atwhich the object is predicted to travel along the predicted path). Theprediction system 224 generates prediction data for an object, forexample, based on state data generated by the perception system 220. Insome examples, the prediction system 224 also considers one or morevehicle poses generated by the pose system 222 and/or the map data 216.

In some examples, the prediction system 224 uses state data indicativeof an object type or classification to predict a trajectory for theobject. As an example, the prediction system 224 can use state dataprovided by the perception system 220 to determine that particularobject (e.g., an object classified as a vehicle). The prediction system224 can provide the predicted trajectories associated with the object(s)to the motion planning system 226.

In some implementations, the prediction system 224 is a goal-orientedprediction system that generates potential goals, selects the mostlikely potential goals, and develops trajectories by which the objectcan achieve the selected goals. For example, the prediction system 224can include a scenario generation system that generates and/or scoresthe goals for an object and a scenario development system thatdetermines the trajectories by which the object can achieve the goals.In some implementations, the prediction system 224 can include amachine-learned goal-scoring model, a machine-learned trajectorydevelopment model, and/or other machine-learned models.

The motion planning system 226 determines a motion plan for the aerialvehicle 200 based at least in part on the predicted trajectoriesassociated with the objects within the surrounding environment of theaerial vehicle 200, the state data for the objects provided by theperception system 220, vehicle poses provided by the pose system 222,and/or the map data 216. Stated differently, given information about thecurrent locations of objects and/or predicted trajectories of objectswithin the surrounding environment of the aerial vehicle 200, the motionplanning system 226 can determine a motion plan for the aerial vehicle200 that best navigates the aerial vehicle 200 relative to the objectsat such locations and their predicted trajectories on acceptable routes.

In some implementations, the motion planning system 226 can evaluatecost functions and/or one or more reward functions for each of one ormore candidate motion plans for the aerial vehicle 200. For example, thecost function(s) can describe a cost (e.g., over time) of adhering to aparticular candidate motion plan while the reward function(s) candescribe a reward for adhering to the particular candidate motion plan.For example, the reward can be of opposite sign to the cost.

Thus, given information about the current locations and/or predictedfuture locations/trajectories of objects, the motion planning system 226can determine a total cost (e.g., a sum of the cost(s) and/or reward(s)provided by the cost function(s) and/or reward function(s)) of adheringto a particular candidate pathway. The motion planning system 226 canselect or determine a motion plan for the aerial vehicle 200 based atleast in part on the cost function(s) and the reward function(s). Forexample, the motion plan that minimizes the total cost can be selectedor otherwise determined. The motion plan can be, for example, a pathalong which the aerial vehicle 200 will travel in one or moreforthcoming time periods. In some implementations, the motion planningsystem 226 can be configured to iteratively update the motion plan forthe aerial vehicle 200 as new sensor data is obtained from the sensors218. For example, as new sensor data is obtained from the sensors 218,the sensor data can be analyzed by the perception system 220, theprediction system 224, and the motion planning system 226 to determinethe motion plan.

Each of the perception system 220, the prediction system 224, the motionplanning system 226, and the pose system 222, can be included in orotherwise a part of the aerial vehicle 200 configured to determine amotion plan based on data obtained from the sensors 218. For example,data obtained by the sensors 218 can be analyzed by each of theperception system 220, the prediction system 224, and the motionplanning system 226 in a consecutive fashion in order to develop themotion plan. While FIG. 2 depicts elements suitable for use in a vehicleautonomy system according to example aspects of the present disclosure,one of ordinary skill in the art will recognize that other vehicleautonomy systems can be configured to determine a motion plan for anautonomous vehicle based on sensor data.

The motion planning system 226 can provide the motion plan to vehiclecontrol systems 228 to execute the motion plan. For example, the vehiclecontrol systems 228 can include pitch control module 230, yaw controlmodule 232, and a throttle control system 234, each of which can includevarious vehicle controls (e.g., actuators or other devices or motorsthat control power) to control the motion of the aerial vehicle 200. Thevarious vehicle control systems 228 can include one or more controllers,control devices, motors, and/or processors.

A throttle control system 234 is configured to receive all or part ofthe motion plan and generate a throttle command. The throttle command isprovided to an engine and/or engine controller, or other propulsionsystem component to control the engine or other propulsion system of theaerial vehicle 200.

The aerial vehicle autonomy system 212 includes one or more computingdevices, such as the computing device 202 which may implement all orparts of the perception system 220, the prediction system 224, themotion planning system 226 and/or the pose system 222. The examplecomputing device 202 can include one or more processors 204 and one ormore memory devices (collectively referred to as memory 206). Theprocessors 204 can be any suitable processing device (e.g., a processorcore, a microprocessor, an Application Specific Integrated Circuit(ASIC), a Field Programmable Gate Array (FPGA), a microcontroller, etc.)and can be one processor or a plurality of processors that areoperatively connected. The memory 206 can include one or morenon-transitory computer-readable storage mediums, such as Random AccessMemory (RAM), Read Only Memory (ROM), Electrically Erasable ProgrammableRead Only Memory (EEPROM), Erasable Programmable Read Only Memory(EPROM), flash memory devices, magnetic disks, etc., and combinationsthereof. The memory 206 can store data 214 and instructions 210 whichcan be executed by the processors 204 to cause the aerial vehicleautonomy system 212 to perform operations. The computing device 202 canalso include a communications interface 208, which can allow thecomputing device 202 to communicate with other components of the aerialvehicle 200 or external computing systems, such as via one or more wiredor wireless networks. Additional descriptions of hardware and softwareconfigurations for computing devices, such as the computing device 202are provided herein.

FIG. 3 is an overview diagram of data flow 300 through a computer systemin one or more of the disclosed embodiments. FIG. 3 shows two dynamicfeature sensors 302 a-b. The dynamic feature sensors 302 a-b may sensefeatures of a geographical region in which the sensors are placed. Forexample, some of the dynamic feature sensors 302 a-b may sense weatherinformation within the respective geographic region. Weather informationmay include temperature, dew point, humidity, wind speed, winddirection, precipitation rate or intensity, barometric pressure, orother weather information. Other dynamic feature sensors 302 a-b mayalso be configured to sense dynamic features of a man-made origin. Forexample, the dynamic feature sensors 302 a-b may be configured to sensesound pressure levels and/or loudness at multiple frequencies. Thedynamic feature sensors 302 a-b, such as traffic sensors, sense dynamicfeatures (such as traffic levels) at multiple points over time, and sendthe sensed information 304 a and sensed information 304 b along with anindication of when the information is collected, to a historical dynamicfeature data store 306.

Third-party sensors 308 may also be relied upon to provide information310 to the historical dynamic feature data store 306. For example,third-party traffic sensors may detect an amount and speed of air, road,rail, or other traffic in a region over time and send the information tothe historical dynamic feature data store 306.

In some aspects, sensors may not be used to obtain information ondynamic features. Instead, in these aspects, some of this informationmay be obtained from other sources. For example, some third-partyorganizations make APIs (e.g. Darksky) or files available that providerecorded weather information for a region or regions. Thus, someembodiments may make use of this existing dynamic feature information topopulate the historical dynamic feature data store 306.

The historical dynamic feature data store 306 accumulates the historicalinformation 304 a-b and 310 over time. This historical information 312is then used to train an untrained model 314. The untrained model 314and trained model 320 represented in FIG. 3 both include, in someembodiments, either alone or in combination, computing hardware andsoftware. For example, in some embodiments, the untrained model 314and/or 320 include one or more of the hardware components discussedbelow with respect to FIG. 14. The untrained model 314 and/or 320 alsoinclude, in these embodiments, instructions that configure hardwareprocessing circuitry (e.g. the processor 1402 discussed below) toperform one or more of the functions attributed to the untrained model314 and/or trained model 314 respectively, as discussed below. In someembodiments, one or more of the untrained model 314 and/or the trainedmodel 320 are integrated into the routing system 323, discussed below.

Additional information regarding geographic regions identified in thehistorical information 312 is provided by a static feature data store316. The static feature data store 316 stores static information forgeographic regions. The static information may include, for example,elevation, topography, percentage vegetation, distance from one or moreroads, highways, or air navigation corridors. Static feature informationfor the region used to train the untrained model 314 are also providedto the untrained model 314 as static feature information 317.

After training of the untrained model 314, a trained model 320 isavailable. The trained model 320 generates noise predictions 321 for aregion. The predictions may include predictions for time varyingloudness over multiple frequencies. The trained model generates thepredictions for a region based on dynamic feature data 322 for theregion and static feature information 324 for the region.

The noise predictions 321 are provided, in some embodiments, to therouting system 323. The routing system 323 stores the predictionsgenerated by the trained model 320 in a datastore 325. In some of thedisclosed embodiments, the routing system 323 further processes thenoise predictions 321 made by the trained model 314 to generate noisemap data. For example, the routing system 323 generates noiseinformation for a plurality of regions based on the noise predictions321. This plurality of regions forms a map in some embodiments. Someembodiments of the routing system 323 further generate a plurality ofroutes for an aircraft based on the noise predictions 321. For example,the routing system 323 generates, in some embodiments, a plurality ofroutes from an origin location to a destination location for an aircraftflight. The routing system 323 then, in some embodiments, compares thebackground information for each of the plurality of routes to determinewhich of the routes is to be used for the aircraft flight. In someembodiments, background noise loudness of each region included in aroute is aggregated. The aggregated noise for a plurality of differentroutes is then compared to determine which route to select for theaircraft flight. For example, some embodiments select a route having thehighest aggregated noise based on the comparison.

The aircraft flight's altitude is also in some cases selected based onthe background noise loudness predicted along a route, with less noisyroutes (below a first predefined noise threshold) causing the aircraftaltitude to be set to a higher value (above a first predefined altitudethreshold) while more noisy routes (above a second predefined noisethreshold) result in a lower altitude (below a second predefinedaltitude threshold). In some embodiments, the first predefined altitudethreshold and the second predefined altitude threshold are equivalent.In some embodiments, the first predefined noise threshold and the secondpredefined noise threshold are equivalent.

In some embodiments, once a route is selected, the routing system 323sends information defining the selected route to the UTM 108, which isdiscussed above.

FIGS. 4A and 4B show noise measurement locations in a metropolitan area.The noise measurements collected can be used to train a model thatpredicts noise as further described below. A map 400 shown in FIG. 4Aincludes noise measurement devices, examples of which are marked as 402a-c. FIG. 4B shows a map 450 indicating locations of noise measurementdevices in a metropolitan area. The noise measurement devices can beconfigured to measure time varying loudness at least every second. Themeasurement devices can be positioned in the geographic region so as tocapture variations and correlation with nearby highway traffic. Somemeasurement devices are mounted at fixed locations and sample soundpressure, in some cases at 32,000 times per second. This sound pressuredata may be applied in offline analysis into fractional octave pressureand loudness data. Some measurement devices are mounted on portableplatforms, that can be repositioned in diverse neighborhoods. Theportable platform measurement devices can be configured to collect ⅓octave band level data and internally process 1/12 octave band loudnessdata once per second. This is increased fidelity collection compared toprevious ⅓ octave or A-weighted collection methods, which havetraditionally used averaging times of one minute or longer.

The loudness data may be collected in tenths of phon. The measurementdevices may be further configured to collect ¼-Cam (Cambridge) specificloudness (¼ Cam is approximately equivalent to 1/12 octave.” Thesemeasurements may be used to illustrate a loudness spectrum. Soundpressure level (SPL) levels may also be collected. For example, themeasurement devices may be configured to collect SPL at each ⅓ octaveband.

Previous work has used sound pressure level to measure noise. Asexplained above, SPL does not accurate measure whether humans find asound annoying. Loudness measurements such as those in InternationalOrganization for Standardization (ISO) 532-3 (e.g., Cambridge basedloudness measurement and more advanced metrics incorporating loudness)are a better method for tracking the perception of sound because thesemetrics consider several factors.

These factors include level-dependence of frequency weighting. Soundpressure levels are traditionally weighted along a curve in order toaccount for a human hearing range. Auditory reception behavesdifferently at different sound levels.

Another factor not considered by use of SPL is spectral loudnesssummation: Two separate sounds can share the same level, but differencesin spectrum may contribute to differences in loudness. Besidessensitivity variations with frequency, and human hearing is lesssensitive to frequencies that are close to frequencies already strongerin the overall sound, and more sensitive to frequencies that are furtheraway from stronger spectral components.

Another factor not considered by SPL is a temporal shape of sound:Separate sounds may share an equivalent level and spectrum, but maystill be perceived very differently. For example, if a particular soundis several decibels less in sound pressure than background noise, thesound may still stand out depending on its temporal shape.

In contrast, stationary or time-varying loudness metrics, derived fromwork at Cambridge University, includes measurements of both short-term(rapid onset) and long-term (memory) reception in the auditory system.These measurements account for both the inertia of the humanphysiological system (human neurons don't fire instantaneously), and theaudibility of “pulses” such as rotor blades. Number and magnitude ofevents is also important in perception, and thus an intermittency ratiocan play a role in gauging perceived acoustic exposure. One model ofloudness was proposed in Moore B. C. J., Glasberg B. R. (1996) Arevision of Zwicker's loudness model. Acustica United with Acta Acustica82: 335-345. The loudness model was further updated in 2006 in GlasbergB R, Moore B C, Prediction of absolute thresholds and equal-loudnesscontours using a modified loudness model, J Acoust Soc Am. 2006 August;120(2):585-8. Time varying loudness (TVL) was described in Stone M. A.,Glasberg B. R., Moore B. C. J., Dynamic aspects of loudness: A real-timeloudness meter. British Journal of Audiology 30: 124 (1996). This wasfurther extended in Glasberg B. R., Moore B. C. J., A model of loudnessapplicable to time-varying sounds. Journal of the Audio EngineeringSociety 50: 331-342 (2002).

The measurement devices discussed above have the capability to measureCambridge based time varying loudness at least every second. Datacollected from several locations is used by the disclosed embodimentsfor a training set. The training set is used to build a loudnesspredictive model. The loudness predictive model predicts a noisemagnitude at any instant in the day based on other inputs includingweather, traffic volume, traffic speed, and distance from a road (e.g.highway).

FIG. 5 shows two example noise measurement devices that are implementedby one or more of the disclosed embodiments. FIG. 5 shows a fixed noisemeasurement device 502 and a moveable noise measurement device 504.Example movable sensors have characteristics including A weatherproofomnidirectional microphone that is installed on a lip of a trunk of anautomobile along with a solar panel on the roof or the trunk lid. Somemicrophones used by the disclosed embodiments have the characteristicsshown in Table 1 below:

TABLE 1 Item Specification Frequency Range 698-960 MHz, 1710-2700 MHzPolarization Vertical Pattern Omnidirectional Cable & Mount MB195Terminations NMO or P-Mount w/Type N(f) Operating Temperature −40° C. to85° C.

A system enclosure of some embodiments has two XLR connectors and apass-through for a USB cable to an LTE modem. One connector is attachedto the microphone, the other to the solar panel. Some embodimentsinclude is a switch that controls power to the data system.

A microphone signal is provided to a USB analog-to-digital converter,which is attached to a USB port of a computing device (e.g. Raspberry Pi3B microcontroller) in some embodiments.

Software is installed on a SD card of the computing device, (e.g.running Raspbian Jesse. The solar panel provides power to a chargecontroller which delivers programmed charge current to an 18AH OdysseyPC680 motorsport/light aircraft battery in some embodiments.

The charge controller incorporates a low voltage disconnect to preventthe battery from being discharged below about 11 volts. Battery capacitycan be greatly reduced if the battery experiences even one deepdischarge. The battery (via a switch on the outer housing) feeds aswitch-mode power supply that provides 5.3 volts to the computingdevice, in at least some embodiments.

Accuracy of predictions provide by the model is improved via thecollection of noise data from acoustically different sites. Thesedifferent sites should still maintain a set of covariates consistentwith a geographic region for which predictions will be made. Examples ofcovariates, one or more of which are employed in various embodiments, incollection of model training data are shown in Table 2 below:

TABLE 2 Name Description Distance from airports Indicates proximity toprivate and public airports. Distance from railroads Indicates proximityto railroads. Use in conjunction with railroad activities by time of dayin at least some embodiments. Distance from roadways Indicates proximityto roadways. Use in conjunction with road density by time of day,vehicle type, and vehicle speed. Distance from military Indicatesproximity to military air traffic flight paths Population densityIndicates population density in different parts of the city at by timeof day different times of the day. For example, a residential area islikely busier on weekends while a commercial zone is likely businessduring business hours. Flight density Indicates variability in airtraffic over time. by time of day (air traffic) Number of airportIndicates variability in noise generated by airport enplanementsoperations over time. This variable is used, in some by time of dayembodiments, in conjunction with an indication of a proximity to saidairport. In some embodiments, this variable is provided via a number ofexpected takeoffs and landings from the airport during time periodsspanning a day.. Railroad operations Indicates variability in noisegenerated by freight rail density by time of day (class I) operationsover time. In some embodiments, this indication is used in conjunctionwith indication of proximity to said railroad. Road density by timeIndicates variability in vehicle traffic throughout the day. of day,vehicle type, Where accessible, use publicly available traffic and speed(traffic) proportions by vehicle type and speed. Proportion of Indicatespercent barren land in region of interest. barren land cover Preferredgranularity of 200 meters. Proportion of Indicates percent developedland in region of interest. developed land cover Preferred granularityof 200 meters. Proportion of Indicates percent forested land in regionof interest. forested land cover Preferred granularity of 200 meters.Proportion of Indicates percent shrubland in region of interest.shrubland cover Preferred granularity of 200 meters. Proportion ofIndicates percent wetland in region of interest. wetland cover Preferredgranularity of 200 meters. Distance from water Indicates straight linedistance to water body in region of bodies (coast, stream, etc.)interest. Preferred granularity of 200 meters. Humidity levels Dynamicexplanatory variable accounting for differences by time of day in airpressure and humidity, which impacts atmospheric acoustic absorption.Precipitation Dynamic covariate accounting for rainfall. by time of dayElevation Indicates atmospheric density, which impacts sound absorption.Wind speed Dynamic covariate accounting for wind. by time of dayAtmospheric pressure Dynamic covariate accounting for atmosphericpressure. by time of day Atmospheric pressure affects sounds' ability totravel through the air.

FIG. 6A. shows observations 600 collected via the measurement devicesdescribed above. These observations show that, at many locations, noiselevels vary significantly throughout the day and for a weekend versus aweekday, as shown in FIG. 5. FIG. 6A shows the peak activity at 6 AM tobe nearly three times as loud as the quiet time three hours earlier.High noise levels may present an opportunity to conduct a high volume ofoperations. In contrast, operations during low background noise periodscan significantly disrupt communities.

Using a predictive model implemented by embodiments of this disclosure,noise map data can be generated based on the predictions from the model.The noise map data can describe the predicted loudness (e.g., backgroundnoise loudness) of a plurality of locations within the geographicregion. To create the noise map data the output of the model 320 candefine the predicted background noise loudness (time varying loudness),as described herein, as well as an associated location (e.g., describedby latitude coordinate, longitude coordinate, attitude, etc.). Themodel's outputted predictions can be aggregated to create the noise mapdata that indicates the predicted background noise loudness at theplurality of locations within a geographic area. For example, the noisemap data can include noise heatmaps of a geographic region as predictedby the model. Examples of these predictions are shown in FIG. 6B andFIGS. 7A-B. FIG. 6B and FIGS. 7A-B show noise levels in a geographicregion 650 and 700 at different times of day. Contrasting FIG. 6B withFIGS. 7A-B, the darker colors associated with FIGS. 7A-B relative toFIG. 6B indicate increased noise levels in the darker regions. FIG. 7Bshows selection of a sky lane based on loudness data, according to atleast some of the disclosed embodiments. A sky lane defines how airtraffic may be routed through the geographic region shown in the image.FIG. 7B shows a first line 705 indicating how the sky lane is configuredwithout loudness considerations. FIG. 7B also shows a second line 710indicating the effect of considering loudness.

As further described herein, the noise map data can be utilized forvarious purposes associated with an aerial vehicle. For example, in someimplementations, the noise map data can be utilized for aerial vehiclerouting and/or sky lane optimization. For example, aerial vehicle routescan be generated based on the noise map data to create routes that wouldmaintain an acceptable level of loudness for the locations along theroute in the geographic area. The acceptable level of loudness may be athreshold (e.g., in decibels or other unit) under which a total noiselevel (e.g., predicted loudness plus aerial vehicle generatedloudness/noise) is to remain below. The threshold may be set by aregulatory body or other authority, a service entity managing an aerialfleet, or the like. The routes and/or sky lanes can be created such thatthe aerial vehicles are routed in a manner not to exceed the acceptablelevel of loudness at any point along the route. Moreover, a sky lane fora route (e.g., a volume around a route in which the aerial vehicle is tostay within) can be generated to ensure that the aerial vehicle stayswithin a threshold distance along a route to maintain an acceptablenoise level.

The noise map data can also, or alternatively, be used to determine oneor more operating constraints of an aerial vehicle. For instance, timeconstraints identifying take-off times, flight travel times, landingtimes, times for a first take-off/landing of the day, times for a lasttake-off/landing of the day, etc. can be determined based on the noisemap data. In particular, the noise map data can provide the predictedloudness at various times of the day and, thus, allow for flight timeschedules to be generated to maintain an acceptable level of loudness.By way of example, the noise map data can help determine at which timein the morning and/or night flights should commence and/or end for theday, and/or what intermediate times may be better to have a loweraggregate nose level (e.g., when school is ending for the day).

Additionally, or alternatively, one or more landing constraints can bedetermined based on the noise map data. For example, the noise map datacan help determine the take-off angle/direction and/or landing angle ofapproach that may best help to maintain a total noise level below thethreshold acceptable level of loudness.

The noise map data can be utilized for assigning vehicles to routeswithin a geographic region and/or determining the flight frequency ofthe aerial vehicles along the routes. For example, the aerial vehiclefleet may include aerial vehicles of different types, makes, models,sizes, and so forth. As such, certain types of aerial vehicles withinthe aerial fleet may generate different levels of noise/loudness as thevehicle takes-off, lands, and/or traverses a route (e.g., due todifferences in propulsion systems, payload capacity, fuel types, etc.).The aerial vehicle operating noise/loudness levels may be acquired andstored (e.g., via the aerial vehicle manufacturer/vendor), measured bysensors as the aerial vehicle operates, and/or calculated based on avehicle model. As described herein, the noise map data can indicate thepredicted loudness at locations along routes within a geographic region(e.g., a background noise layer), which can allow for the determinationof how much additional noise/loudness can be added by an operatingaerial vehicle (e.g., an aerial vehicle noise layer) to remain below anacceptable level of loudness. Based on such a determination, particularaerial vehicles can be selected and assigned to routes in order tomaintain a total noise level below the acceptable level of loudness.

In some implementations, the frequency of the flights along aroute/within a geographic region can be determined based on the noisemap data. For instance, the number of times that aerial vehiclestraverse a route can be determined to maintain an acceptable level ofloudness in the locations along the route/within the mapped geographicregion.

FIG. 8 shows an example machine learning module 800 according to someexamples of the present disclosure. Machine learning module 800 utilizesa training module 810 and a prediction module 820. Training module 810inputs historical information 830 into feature determination module850A. The historical information 830 may be labeled. As described above,historical information may include historical measurements of dynamicfeature information for a plurality of geographical regions. The dynamicfeature information may be collected over a plurality of historical timeperiods (training time periods), which are also indicated in thehistorical information 830, at least in some embodiments. Static featureinformation for the geographical regions is also used in training of themodel.

Feature determination module 850A determines features 860 from thishistorical information 830. Stated generally, features 860 are a set ofthe information input and is information determined to be predictive ofa particular outcome. In some examples, the features 860 include all thehistorical activity data, but in other examples, the features 860include a subset of the historical activity data.

Note that some features may initially be provided during different unitsof resolution. For example, in some embodiments, weather data isreceived in hex-level, road traffic is obtained as locations intwo-dimensional space, air traffic information is obtained as locationsin three-dimensional space, and some static features are provided on agrid. To aggregate these data, some embodiments perform noiseattenuation for road traffic and air traffic (e.g. log method). Someembodiments apply linear weights to features represented on the grid.Some embodiments provide for a tunable radius to aggregate differentfeatures for a given point of interest.

Some embodiments tailor the historical activity data that is used totrain the model so as to reduce multi co-linearity. During developmentof the disclosed embodiments, strong correlations between variousfeatures were identified. Thus, some embodiments use one or more ofPrinciple Component Analysis (PCA) or recursive elimination to removeless relevant correlated features. These embodiments demonstratessignificant performance improvements when compared to those embodimentsthat do not function to reduce multi co-linearity.

The machine learning algorithm 870 produces a model 806 (e.g. equivalentto trained model 314 in some aspects) based upon the features 860 andthe label.

In the prediction module 820, current information 890 is input to thefeature determination module 850B. Current information 890 may includedynamic feature information and static feature information for aparticular geographic region. The particular geographic region may ormay not have been included in the historical information 830.

Feature determination module 850B may determine the same set of featuresor a different set of features from the current information 890 asfeature determination module 850A determined from historical information830. In some examples, feature determination module 850A and 850B arethe same module. Feature determination module 850B produces a featurevector 815, which is input into the model 806 to generate a loudnessprediction 895 for the geographic region. The loudness prediction 895includes predictions for multiple frequencies. In one exampleembodiment, the training module 810 may operate in an offline manner totrain the model 806. The prediction module 820, however, may be designedto operate in an online manner. It should be noted that the model 806may be periodically updated via additional training and/or userfeedback.

The machine learning algorithm 870 may be selected from among manydifferent potential supervised or unsupervised machine learningalgorithms. Examples of supervised learning algorithms includeartificial neural networks, Bayesian networks, instance-based learning,support vector machines, decision trees (e.g., Iterative Dichotomiser 3,C4.5, Classification and Regression Tree (CART), Chi-squared AutomaticInteraction Detector (CHAID), and the like), random forests, linearclassifiers, quadratic classifiers, k-nearest neighbor, linearregression, logistic regression, hidden Markov models, models based onartificial life, simulated annealing, and/or virology. Examples ofunsupervised learning algorithms include expectation-maximizationalgorithms, vector quantization, and information bottleneck method.Unsupervised models may not have a training module 810. In an exampleembodiment, a regression model is used and the model 806 is a vector ofcoefficients corresponding to a learned importance for each of thefeatures in the features 860 and the feature vector 815. To calculate ascore, a dot product of the feature vector 815 (features are notnecessarily represented as vectors in some embodiments) and the vectorof coefficients of the model 806 is taken.

The disclosed embodiments use a variety of features to train a model fornoise prediction. In one example embodiment, these features includestatic features of a region and dynamic features for the region overmultiple periods of time. In some embodiments, a time identifying a timeperiod is provided in various formats, including, for example, hoursfrom January 1 or a concatenation of month, day, and hour, or aconcatenation of a weekend or weekday indicator with an indication of anhour of the day. In some embodiments, training input includes staticlocation features (e.g. elevation), and time-dependent dynamic locationfeatures (e.g. traffic volume). In some embodiments, the inputs arelocation covariates (stationary and time-dependent) or location (e.g.latitude, longitude), time representation (e.g. hours from 1/1/1970 ormonth+day+hour or in some embodiments a weekend/weekday indicator and/oran hour indication), or location covariates (stationary), timerepresentation. Location covariates provide for predictions in newlocations. In some embodiments, use of time-dependent covariatesrequires predicting covariate values when predicting future noiselevels. In various embodiments, additional training inputs includes oneor more of traffic volume, traffic speed, temperature, dewpoint,humidity, wind speed, wind direction, precipitation intensity, pressure,distance to a body of water (e.g. a stream or lake), elevation, distancefrom road(s), distance from railroad(s), distance from a coast, ordistance from airport(s). The training set data generated by themeasurement devices described above with respect to FIG. 1, in someembodiments, include features (both dynamic and static) and labels (SPLvalues or TVL spectra data or spectral data) for a location (e.g.latitude and longitude) and an hour of a day. Some embodiments providenoise measures in L90 form. L90 is a highest sound level exceeded during90% of a measured time period. A sound measurement in L90 form isgenerally considered to represent background or ambient level of noiseof a measured environment. One challenge with building a training set isto map different datasets to the same location. Some data sources use arandom location, some of which fall on a road segment. Weather data useshexagon identifiers and NPS data uses latitude and longitude for grid ofpoints on a map a predetermined number of meters apart. Some of thedisclosed embodiments include a process to unify these differentlocation systems to minimize data loss and improve efficiency.

Some of the disclosed embodiments train the model using noise datagathered from flight track information obtained from the FederalAviation Administration (FAA). The flight track information allowsdetermination of distances between aircraft generating noise and noisemeasurements made by ground-based sensors. The flight track informationis in the form (x, y, z, t) in at least some embodiments, with x, y, zdefining a location of an aircraft in three dimension space (e.g. x, yare latitude, longitude, and z represents altitude either above groundlevel (AGL) or MSL (mean sea level) form. In some embodiments, one ormore noise measurements are quantified based on a noise impact due toair traffic as:

Normalized noise impact due to air traffic at time t:

$N_{t} = {\sum\frac{1}{D^{2} + z^{2}}}$

where:

-   -   N_(t) is a normalized noise measurement at time t,    -   D is a Haversine distance between an aircraft location and a        sensor taking the measurement, and    -   z is the altitude of the flight.

The disclosed embodiments can predict, via the trained model, for agiven location and a given time, (e.g. a one-hour period) a soundpressure level (SPL) in dBA. This prediction generates one value, ascalar. The disclosed embodiments can further predict time varyingloudness via the same trained model. This prediction can includemultiple values (e.g. 150), which are represented as a vector in someembodiments. In various embodiments, either separate models for eachprediction or a single model with multiple outputs could be utilized toobtain predictions of SPL and TVL. One TVL model generates via thedisclosed subject matter results in an RMSE of 3.39, and R-squared of0.69. This embodiment uses a decision-tree based model.

Values for different bands of TVL include some correlation. Thus, TVLspectrum output of the model is represented as a vector in someembodiments, with each unit in the vector representing a ⅓ octave band(e.g. 32). Alternatively, a model for each TVL band could be separatelytrained. Several model types can be used in example embodiments,including a linear model (e.g. linear regression, logistic regression),support vector regression, random forest regression, gaussian process,free-based models, multilayer perceptron (MLP)/neural network (NN), orensembles of models.

FIG. 9 is a flowchart of a process 900 for processing training databefore using the training data to train a model. In some aspects, theprocess 900 is performed by hardware processing circuitry. For example,in some embodiments, instructions stored in an electronic memoryconfigure the hardware processing circuitry to perform one or more ofthe functions discussed below with respect to FIG. 9 and the process900.

After start operation 902, the process 900 moves to operation 905. Inoperation 905, a radius is defined. The radius is implemented as atunable parameter in some embodiments. The radius defines a distancefrom a sensor that will be searched for relevant features (from othersensors).

In operation 910, a location of a sensor is identified. In one exampleembodiment, process 900 iterates through a set of sensors, withoperation 910 identifying a location of a sensor currently beinganalyzed by the process 900.

In operation 915, a region is determined based on the location and theradius. The location and radius define a circular region around thesensor location.

In operation 920, feature data points within the region are identified.In some aspects, the feature data points are aggregated to a singlevalue for a particular location/time. In some aspects, equation (1)below is utilized for the aggregation, where n points are identified ina region R_(i):

${f_{i} = {\sum\limits_{j = 1}^{n}{\frac{1}{e^{{- \alpha}\; d_{j}}}f_{i}^{j}}}},\mspace{14mu} {\forall{i \in F}}$

where:

F is a set of all feature names

In operation 925, a weighted average is generated from the data points.In some aspects, a principled weight function is applied. In someaspects, a negative exponential weight is utilized to approximate soundattenuation with distance. Thus, in some embodiments, for a feature iwith distance d from the sensor location, the weight is w_(i)=1/e^(αd).The alpha value (α) is based on environmental conditions within theregion. Example environmental conditions comprise whether theenvironment is predominately urban, suburban, or rural, average heightof buildings, and so forth. Some aspects use an alpha (α) value of two(2).

In some other embodiments, one of the following weight equations is used

${wi} = {{\frac{1}{d_{j}}\mspace{14mu} {or}\mspace{14mu} {wi}} = {\frac{1}{d_{j}^{2}}.}}$

In some embodiments, weights are normalized. In these embodiments, theweighted sum will be divided by the sum of the weights, or

$\sum\limits_{j = 1}^{n}{\frac{1}{d_{j}}\mspace{14mu} {or}\mspace{14mu} {\sum\limits_{j = 1}^{n}{\frac{1}{d_{j}^{2}}.}}}$

In operation 930, the weighted average is added to the training data.Decision operation 940 determines if additional sensor locations are tobe evaluated. If so, process 900 returns to operation 910. Otherwise,process 900 moves to end operation 945.

FIG. 10 shows one observation of analysis of the training set describedabove. FIG. 10 shows a relatively strong correlation between soundpressure level (SPL) and traffic volume, given that SPL is high duringrush hour and generally lower in the evening hours.

FIG. 11 shows a correlation matrix of model features in accordance withone embodiment. The correlation matrix 1100 shows correlations betweentraffic volume and speed (negative correlation) and dewpoint andtemperature (positive). The correlation matrix 1000 also shows acorrelation between speed and humidity.

To determine an appropriate model for loudness prediction, severalanalyses are done using a variety of different models. Initial teststrained a multiple linear regression model using seventy percent of thetraining data for training and thirty percent of the training data fortesting. The results are shown in Table 3 below:

TABLE 3 Metric Value Mean Squared Error (MSE) 9.28 Mean Absolute Error(MAE) 2.28 Root Mean Squared Error (RMSE) 3.05 R-Squared 0.86The metrics above show the linear regression model provided relativelygood performance when predicting SPL. Further tests were performed tounderstand how well the model predicted noise for a time period notincluded in the training data. To perform this test, training data for afirst time period is withheld from use in training the model, and thenused to predict the accuracy of predictions made by the model for thatfirst time period. The results are shown in table 4 below:

TABLE 4 Metric Value Root Mean Squared Error (RMSE) 3.28 R-Squared 0.82

The analysis above suggests the model can accurately predict SPL for atime period even if training data for that time period is absent.

FIGS. 12A-B show a flowchart of a process 1200 for predicting timevarying background loudness on multiple frequencies. In some aspects,one or more of the functions discussed below with respect to process1200 and FIGS. 12A-B are performed by hardware processing circuitry. Forexample, instructions stored in a hardware memory configure the hardwareprocessing circuitry to perform one or more of the functions discussedbelow in at least some embodiments. In some embodiments, one or more ofthe functions discussed below are performed by one or more of thetrained model 320, and/or the routing system 323, both of which arediscussed above with respect to FIG. 3.

After start operation 1205, process 1200 moves to operation 1210, whichreceives measurements of dynamic feature data for a geographic region.

In some embodiments, the dynamic feature data includes weather data inthe region. Examples of weather data include one or more of temperature,dewpoint, barometric pressure, wind speed, wind direction, sun position,precipitation rate, percentage snow cover, snow thickness, or otherweather data. The National Weather Service of the United States makesweather information available via a web service. Similar services areavailable in other jurisdictions. Some embodiments obtain weather datafrom a web service and thus receive the weather data in operation 1210.

The dynamic feature data includes, in some embodiments, noiseinformation relating to man-made sources, or man-made feature data.Examples of man-made feature data include traffic speed and/or trafficamount on one or more roads, aircraft speed, rail speed, and/or trafficamount over the region. In some embodiments, the man-made feature datais received, at least indirectly, from one or more data sources. Forexample, current traffic data is available from a variety of webservices maintained by local governments. Some embodiments of operation1210 interface with these services to obtain information on trafficdata. Similarly, the Federal Aviation Administration (FAA) maintains webservices that provide aircraft flight information. Similar services areprovided in jurisdictions outside the United States. Current railtraffic information is available via web services provided by railoperators. Operation 1210 accesses one or more of these services, in atleast some embodiments, to obtain or receive dynamic man-made featuredata.

In one example embodiment, dynamic feature data is measured and/orreceived in operation 1210 is for multiple different periods of time.For example, in some aspects, measurements are received periodically,such as every hour, half hour, or other time period.

In operation 1212, static features of the geographic region aredetermined. Examples of static features include one or more ofelevation, distance from one or more roads, distance from one or moreairports, a percentage vegetation in the region, composition of roads inthe region (e.g. concrete, asphalt or cobble stone), distance to arailroad, distance to a coast (ocean or great lake), distance to a bodyof water (e.g. lake or stream). As discussed above with respect to FIG.3, in some embodiments, operation 1212 reads static feature data from astatic feature data store, such as the static feature data store 316,discussed above with respect to FIG. 3.

In operation 1215, the dynamic features and static features are providedto a model. For example, FIG. 3 shows a data flow that provides dynamicfeatures (e.g. dynamic feature data 322) and static feature information324 to the trained model 314. In some embodiments, providing dynamic andstatic features to the model include the trained model reading thedynamic and static features from data sources for this information, forexample, as described above with respect to FIG. 3. As shown in FIG. 3,the trained model reads the static feature information for one or moregeographic regions from a static feature data store 316 in someembodiments. Dynamic feature data for the region is also received by thetrained model 320 (as dynamic feature data 322). As discussed above, thedynamic feature data is received, in some embodiments, from web servicesthat provide the dynamic feature data, or directly from sensors whichmeasure the dynamic feature information. As discussed above with respectto FIG. 3 and/or FIG. 8, the model is trained based on historical datacollected from a plurality of sensors. The sensors detect time varyingloudness across multiple frequencies in a plurality of geographicregions. Some of the sensors are configured to collected weather data,such as the temperature, dewpoint, humidity, wind speed, wind direction,and other weather data during multiple time periods. The detected timevarying loudness and weather data is correlated according to a time atwhich the loudness and weather information is collected. Thus, thehistorical data describes, over a plurality of time periods, weatherdata, traffic data, and noise information for a particular region.

In one example embodiment, the historical data used to train the modelalso includes static features of the regions. For example, someembodiments obtain static information for a plurality of differentregions, including amount of vegetation in a region, an amount (e.g.percent coverage) of hardscape (e.g. concrete, asphalt, buildings) in aregion, or other static features as discussed above. In someembodiments, the static features used to train the model are obtainedfrom a static feature data store, such as the static feature data store316, discussed above with respect to FIG. 3.

Operation 1218 then predicts background noise loudness based on outputfrom the trained model (e.g. trained model 320). The predictedbackground noise loudness is specific to defined time period, ageographic region or for any given location, based on weatherinformation for the region, static features of the region, and dynamicfeatures for the region, one or more of which are provided to the modelas input in at least some embodiments, as described above. In someembodiments, the defined time period is a “current time period,” basedon when the prediction is performed. In other embodiments, the definedtime period is passed to the model as input and the model is configuredto generate the predicted background noise loudness during the definedtime period based on the input.

The background noise loudness is predicted in operation 1218 for aparticular time and a particular date within a defined time period. Insome embodiments, the particular time, and/or particular date are passedto the model as input.

In operation 1220, noise map data is generated based on the modelpredicted background noise loudness within a geographic region (asoutputted by the trained model). This can include, for example,aggregating the predicted loudness values at a plurality of locations togenerate a heatmap (or other type of representation) describing theloudness at locations within the geographic region at one or more times,time ranges, and so forth. The predicted background noise loudnessincludes, in various embodiments, one or more of stationary,time-varying, partial, or partial specific noise loudness.

In some embodiments, generating noise map data in operation 1220includes generating predicted background noise loudness for a pluralityof geographic regions. The plurality of geographic regions adjoin eachother (e.g. in a grid orientation), in some embodiments, so as to form amap of a portion of the earth's surface. The noise map data thusincludes this plurality of geographic regions and their correspondingpredicted background noise loudness.

In operation 1222, one or more aerial vehicle routes and/or sky lanesfor the geographic area are determined based on the model predictedloudness and/or noise map data. This can include determining thelocations/altitudes of the flight routes (and sky lanes associatedtherewith) so as to maintain an acceptable level of loudness in thegeographic region, as described herein.

Some embodiments of operation 1222 determine an origin and destinationof an aircraft flight. These embodiments then determine a plurality ofpossible routes between the origin and destination. Each of theplurality of possible routes includes a plurality of differentgeographic regions through which the aircraft travels to reach thedestination from the origin location. Some of the embodiments of process1200 generate predicted background noise in each of these geographicregions, based on dynamic and static feature data of each of therespective regions. This information is provided to the trained modeland the model predicts the background noise for each of the geographicregions.

Some embodiments then select one of the plurality of routes based on thepredicted background noise. For example, some embodiments aggregatepredicted background noise along each of the routes (e.g. based on thepredicted background noise of each region through which the aircraftpasses when executing the route), and select the route with the mostaggregated noise. Some embodiments set a regions predicted backgroundnoise to a predetermined maximum value before aggregating noise along aroute, to limit bias in selection caused by particularly noisy regions.

As discussed above, some embodiments define flight routes to specifyhigher altitudes when traveling over a geographic region with arelatively low level of predicted background noise (e.g. noise below apredefined threshold). These embodiments define flight routes to specifylower altitudes when traveling over a geographic region with arelatively higher level of predicted background noise (e.g. above apredefined threshold). In some embodiments, the altitude for a region isalso based on altitudes of neighboring regions along a route, so as toavoid too frequent altitude changes during the route.

In operation 1224, one or more aerial vehicle operating constraints aredetermined based on the predicted background loudness and/or noise mapdata. For instance, the predicted loudness and/or noise map data isutilized to determine aerial vehicle constraints, including operatingconstraints such as, for example: take-off times, in-flight traveltimes, landing times, times for a first take-off/landing of the day,times for a last take-off/landing of the day, take-off angle/direction,landing angle of approach, and/or other operating constraints. As oneexample of a vehicle constraint, a minimum climb rate of an aircraftafter take-off is set, in some embodiments, based on a predictedbackground noise of one or more geographic regions the aircraft willoverfly before reaching a cruising altitude. In some embodiments, apredicted background noise below a predefined noise threshold results ina minimum climb rate above a first rate threshold. In these embodiments,if the predicted background noise of a region overflown by the aircraftbefore it reaches its cruising altitude is above a second predefinednoise threshold, the minimum climb rate is set below a second ratethreshold. In some embodiments, the first rate threshold and the secondrate threshold are equivalent.

As another example, a maximum range attribute of an aircraft requiredfor a route between an origin and destination is constrained based on adistance from the origin to the destination along a selected route. Asan example, some embodiments generate a plurality of different routesbetween the origin and the destination. In some cases, a route having alonger distance is selected, for example, so as to avoid a geographicregion having a relatively low background noise. This longer route thuswill require an aircraft having sufficient range to perform the route.

In operation 1226, aerial vehicles are assigned to aerial routes basedon the predicted background loudness and/or the noise map data.

For example, as discussed above, constraints on aerial vehicles aredetermined based on the routes selected for travel between an origin anddestination. In the example of climb rate above, an aerial vehicle isselected to perform a route based on a specification of the aerialvehicle indicating the aerial vehicle can climb at or above a minimumclimb rate determined for the route (which is, in some embodiments,based on predicted background noise loudness of one or more geographicregions overflown by the aerial vehicle while performing the route).Similarly, a distance between an origin and destination when performinga selected route requires an aerial vehicle having a range that meets orexceeds the distance.

As an additional example, a first aerial route may include locationswith higher levels of predicted loudness than a second aerial route. Theaerial vehicle fleet can include a first aerial vehicle and a secondaerial vehicle. The first aerial vehicle can be a different make, model,type, and so forth than the second aerial vehicle. The first aerialvehicle can produce a higher level of noise/loudness when operating(e.g., taking-off, landing, flying, etc.) than the second aerialvehicle. The first aerial vehicle (the louder vehicle) can be assignedto the first aerial route (the louder route) because the predictedbackground loudness at the locations along the first aerial route aremore compatible with the operation of the first aerial vehicle. In thiscase, compatibility is determined by proportionality between thepredicted background loudness of regions along the first aerial routeand the loudness of the aerial vehicle. The second aerial vehicle (thequieter vehicle) can be assigned to the second aerial route (the quieterroute) because the predicted loudness at the locations along the secondaerial route and the noise generated by operation of the second aerialvehicle are compatible.

In another example, the first aerial vehicle may be assigned to thesecond aerial route because the locations along the second aerial routemay have a higher acceptable level of loudness. The second aerialvehicle may be assigned to the first aerial route because the locationsalong the first aerial route may have a lower acceptable level ofloudness.

In operation 1228, a frequency of aerial vehicle flights are determinedbased on the predicted background loudness and/or the noise map data.For instance, a number of times that aerial vehicles traverse a firstroute and/or second route are determined to maintain an acceptable levelof background noise loudness in the locations along those routes, in atleast some embodiments. For example, some embodiments limit a rate atwhich aircraft overfly a particular geographic region. This rate isadjusted, in some embodiments, based on a time of day and/or whether theday is a week day or a weekend day. The rate is also adjusted, in someembodiments, based on predicted background noise loudness.

After operation 1228, process 1200 moves to end operation 1230.

FIG. 13 is a block diagram showing one example of a softwarearchitecture 1300 for a computing device. The software architecture 1302may be used in conjunction with various hardware architectures, forexample, as described herein. FIG. 13 is merely a non-limiting exampleof a software architecture 1302 and many other architectures may beimplemented to facilitate the functionality described herein. Arepresentative hardware layer 1304 is illustrated and can represent, forexample, any of the above-referenced computing devices. In someexamples, the hardware layer 1304 may be implemented according to anarchitecture 1400 of FIG. 14 and/or the software architecture 1302 ofFIG. 13.

The representative hardware layer 1304 comprises one or more processingunits 1306 having associated executable instructions 1308. Theexecutable instructions 1308 represent the executable instructions ofthe software architecture 1302, including implementation of the methods,modules, components, and so forth of FIGS. 1-12. The hardware layer 1304also includes memory and/or storage modules 1310, which also have theexecutable instructions 1308. The hardware layer 1304 may also compriseother hardware 1312, which represents any other hardware of the hardwarelayer 1304, such as the other hardware illustrated as part of thesoftware architecture 1300.

In the example architecture of FIG. 13, the software architecture 1302may be conceptualized as a stack of layers where each layer providesparticular functionality. For example, the software architecture 1302may include layers such as an operating system 1314, libraries 1316,frameworks/middleware 1318, applications 1320, and a presentation layer1344. Operationally, the applications 1320 and/or other componentswithin the layers may invoke API calls 1324 through the software stackand receive a response, returned values, and so forth illustrated asmessages 1326 in response to the API calls 1324. The layers illustratedare representative in nature and not all software architectures have alllayers. For example, some mobile or special-purpose operating systemsmay not provide a frameworks/middleware 1318 layer, while others mayprovide such a layer. Other software architectures may includeadditional or different layers.

The operating system 1314 may manage hardware resources and providecommon services. The operating system 1314 may include, for example, akernel 1328, services 1330, and drivers 1332. The kernel 1328 may act asan abstraction layer between the hardware and the other software layers.For example, the kernel 1328 may be responsible for memory management,processor management (e.g., scheduling), component management,networking, security settings, and so on. The services 1330 may provideother common services for the other software layers. In some examples,the services 1330 include an interrupt service. The interrupt servicemay detect the receipt of a hardware or software interrupt and, inresponse, cause the software architecture 1302 to pause its currentprocessing and execute an ISR when an interrupt is received. The ISR maygenerate an alert.

The drivers 1332 may be responsible for controlling or interfacing withthe underlying hardware. For instance, the drivers 1332 may includedisplay drivers, camera drivers, Bluetooth® drivers, flash memorydrivers, serial communication drivers (e.g., Universal Serial Bus (USB)drivers), Wi-Fi® drivers, NFC drivers, audio drivers, power managementdrivers, and so forth depending on the hardware configuration.

The libraries 1316 may provide a common infrastructure that may be usedby the applications 1320 and/or other components and/or layers. Thelibraries 1316 typically provide functionality that allows othersoftware modules to perform tasks in an easier fashion than byinterfacing directly with the underlying operating system 1314functionality (e.g., kernel 1328, services 1330, and/or drivers 1332).The libraries 1316 may include system libraries 1334 (e.g., C standardlibrary) that may provide functions such as memory allocation functions,string manipulation functions, mathematic functions, and the like. Inaddition, the libraries 1316 may include API libraries 1336 such asmedia libraries (e.g., libraries to support presentation andmanipulation of various media formats such as MPEG4, H.264, MP3, AAC,AMR, JPG, and PNG), graphics libraries (e.g., an OpenGL framework thatmay be used to render 2D and 3D graphic content on a display), databaselibraries (e.g., SQLite that may provide various relational databasefunctions), web libraries (e.g., WebKit that may provide web browsingfunctionality), and the like. The libraries 1316 may also include a widevariety of other libraries 1338 to provide many other APIs to theapplications 1320 and other software components/modules.

The frameworks/middleware 1318 (also sometimes referred to asmiddleware) may provide a higher-level common infrastructure that may beused by the applications 1320 and/or other software components/modules.For example, the frameworks/middleware 1318 may provide variousgraphical user interface (GUI) functions, high-level resourcemanagement, high-level location services, and so forth. Theframeworks/middleware 1318 may provide a broad spectrum of other APIsthat may be used by the applications 1320 and/or other softwarecomponents/modules, some of which may be specific to a particularoperating system or platform.

The applications 1320 include built-in applications 1340 and/orthird-party applications 1342. Examples of representative built-inapplications 1340 may include, but are not limited to, a contactsapplication, a browser application, a book reader application, alocation application, a media application, a messaging application,and/or a game application. The third-party applications 1342 may includeany of the built-in applications 1340 as well as a broad assortment ofother applications. In a specific example, the third-party application1342 (e.g., an application developed using the Android™ or iOS™ softwaredevelopment kit (SDK) by an entity other than the vendor of theparticular platform) may be mobile software running on a mobileoperating system such as iOS™, Android™ Windows® Phone, or othercomputing device operating systems. In this example, the third-partyapplication 1342 may invoke the API calls 1324 provided by the mobileoperating system such as the operating system 1314 to facilitatefunctionality described herein.

The applications 1320 may use built-in operating system functions (e.g.,kernel 1328, services 1330, and/or drivers 1332), libraries (e.g.,system libraries 1334, API libraries 1336, and other libraries 1338), orframeworks/middleware 1318 to create user interfaces to interact withusers of the system. Alternatively, or additionally, in some systems,interactions with a user may occur through a presentation layer, such asthe presentation layer 1344. In these systems, the application/module“logic” can be separated from the aspects of the application/module thatinteract with a user.

Some software architectures use virtual machines. For example, systemsdescribed herein may be executed using one or more virtual machinesexecuted at one or more server computing machines. In the example ofFIG. 13, this is illustrated by a virtual machine 1348. A virtualmachine creates a software environment where applications/modules canexecute as if they were executing on a hardware computing device. Thevirtual machine 1348 is hosted by a host operating system (e.g., theoperating system 1314) and typically, although not always, has a virtualmachine monitor 1346, which manages the operation of the virtual machine1348 as well as the interface with the host operating system (e.g., theoperating system 1314). A software architecture executes within thevirtual machine 1348, such as an operating system 1350, libraries 1352,frameworks/middleware 1354, applications 1356, and/or a presentationlayer 1358. These layers of software architecture executing within thevirtual machine 1348 can be the same as corresponding layers previouslydescribed or may be different.

FIG. 14 is a block diagram illustrating a computing device hardwarearchitecture 1400, within which a set or sequence of instructions can beexecuted to cause a machine to perform examples of any one of themethodologies discussed herein. The hardware architecture 1400 describesa computing device for executing the vehicle autonomy system, describedherein. In some embodiments, the hardware architecture 1400 is utilizedby one or more of the untrained model 314, trained model 320 and/or therouting system 323, and/or the UTM 108, discussed above. In someembodiments, one or more of the untrained model 314, trained model 320,and routing system 323 are integrated into a single computing device,such as that represented by hardware architecture 1400. In someembodiments, each of the untrained model 314, trained model 320, androuting system 323 are implemented on physically separate and distinctcomputing devices, with each of these distinct computing devicesincluding one or more of the components of hardware architecture 1400,discussed below. In some embodiments, one or more of the functionsdiscussed above and attributed to one or more of the untrained model314, trained model 320, and/or routing system 323 are performed by ashared group or “pool” of hardware devices, each of the hardware devicesincluding one or more of the components discussed below with respect toFIG. 14.

The architecture 1400 may operate as a standalone device or may beconnected (e.g., networked) to other machines. In a networkeddeployment, the architecture 1400 may operate in the capacity of eithera server or a client machine in server-client network environments, orit may act as a peer machine in peer-to-peer (or distributed) networkenvironments. The architecture 1400 can be implemented in a personalcomputer (PC), a tablet PC, a hybrid tablet, a set-top box (STB), apersonal digital assistant (PDA), a mobile telephone, a web appliance, anetwork router, a network switch, a network bridge, or any machinecapable of executing instructions (sequential or otherwise) that specifyoperations to be taken by that machine.

The example architecture 1400 includes a processor unit 1402 comprisingat least one processor (e.g., a central processing unit (CPU), agraphics processing unit (GPU), or both, processor cores, computenodes). The architecture 1400 may further comprise a main memory 1404and a static memory 1406, which communicate with each other via a link1408 (e.g., bus). The architecture 1400 can further include a videodisplay unit 1410, an input device 1412 (e.g., a keyboard), and a UInavigation device 1414 (e.g., a mouse). In some examples, the videodisplay unit 1410, input device 1412, and UI navigation device 1414 areincorporated into a touchscreen display. The architecture 1400 mayadditionally include a storage device 1416 (e.g., a drive unit), asignal generation device 1418 (e.g., a speaker), a network interfacedevice 1420, and one or more sensors (not shown), such as a GlobalPositioning System (GPS) sensor, compass, accelerometer, or othersensor.

In some examples, the processor unit 1402 or another suitable hardwarecomponent may support a hardware interrupt. In response to a hardwareinterrupt, the processor unit 1402 may pause its processing and executean ISR, for example, as described herein.

The storage device 1416 includes a machine-readable medium 1422 on whichis stored one or more sets of data structures and instructions 1424(e.g., software) embodying or used by any one or more of themethodologies or functions described herein. The instructions 1424 canalso reside, completely or at least partially, within the main memory1404, within the static memory 1406, and/or within the processor unit1402 during execution thereof by the architecture 1400, with the mainmemory 1404, the static memory 1406, and the processor unit 1402 alsoconstituting machine-readable media.

Executable Instructions and Machine-Storage Medium

The various memories (i.e., 1404, 1406, and/or memory of the processorunit(s) 1402) and/or storage device 1416 may store one or more sets ofinstructions and data structures (e.g., instructions) 1424 embodying orused by any one or more of the methodologies or functions describedherein. These instructions, when executed by processor unit(s) 1402cause various operations to implement the disclosed examples.

As used herein, the terms “machine-storage medium,” “device-storagemedium,” “computer-storage medium” (referred to collectively as“machine-storage medium 1422”) mean the same thing and may be usedinterchangeably in this disclosure. The terms refer to a single ormultiple storage devices and/or media (e.g., a centralized ordistributed database, and/or associated caches and servers) that storeexecutable instructions and/or data, as well as cloud-based storagesystems or storage networks that include multiple storage apparatus ordevices. The terms shall accordingly be taken to include, but not belimited to, solid-state memories, and optical and magnetic media,including memory internal or external to processors. Specific examplesof machine-storage media, computer-storage media, and/or device-storagemedia 1422 include non-volatile memory, including by way of examplesemiconductor memory devices, e.g., erasable programmable read-onlymemory (EPROM), electrically erasable programmable read-only memory(EEPROM), FPGA, and flash memory devices; magnetic disks such asinternal hard disks and removable disks; magneto-optical disks; andCD-ROM and DVD-ROM disks. The terms machine-storage media,computer-storage media, and device-storage media 1422 specificallyexclude carrier waves, modulated data signals, and other such media, atleast some of which are covered under the term “signal medium” discussedbelow.

Signal Medium

The term “signal medium” or “transmission medium” shall be taken toinclude any form of modulated data signal, carrier wave, and so forth.The term “modulated data signal” means a signal that has one or more ofits characteristics set or changed in such a matter as to encodeinformation in the signal.

Computer-Readable Medium

The terms “machine-readable medium,” “computer-readable medium” and“device-readable medium” mean the same thing and may be usedinterchangeably in this disclosure. The terms are defined to includeboth machine-storage media and signal media. Thus, the terms includeboth storage devices/media and carrier waves/modulated data signals.

The instructions 1424 can further be transmitted or received over acommunications network 1426 using a transmission medium via the networkinterface device 1420 using any one of a number of well-known transferprotocols (e.g., HTTP). Examples of communication networks include aLAN, a WAN, the Internet, mobile telephone networks, plain old telephoneservice (POTS) networks, and wireless data networks (e.g., Wi-Fi, 3G, 4GLTE/LTE-A, 5G or WiMAX networks). The term “transmission medium” shallbe taken to include any intangible medium that is capable of storing,encoding, or carrying instructions for execution by the machine, andincludes digital or analog communications signals or other intangiblemedia to facilitate communication of such software.

Throughout this specification, plural instances may implementcomponents, operations, or structures described as a single instance.Although individual operations of one or more methods are illustratedand described as separate operations, one or more of the individualoperations may be performed concurrently, and nothing requires that theoperations be performed in the order illustrated. Structures andfunctionality presented as separate components in example configurationsmay be implemented as a combined structure or component. Similarly,structures and functionality presented as a single component may beimplemented as separate components. These and other variations,modifications, additions, and improvements fall within the scope of thesubject matter herein.

Various components are described in the present disclosure as beingconfigured in a particular way. A component may be configured in anysuitable manner. For example, a component that is or that includes acomputing device may be configured with suitable software instructionsthat program the computing device. A component may also be configured byvirtue of its hardware arrangement or in any other suitable manner.

The above description is intended to be illustrative, and notrestrictive. For example, the above-described examples (or one or moreaspects thereof) can be used in combination with others. Other examplescan be used, such as by one of ordinary skill in the art upon reviewingthe above description. The Abstract is to allow the reader to quicklyascertain the nature of the technical disclosure. It is submitted withthe understanding that it will not be used to interpret or limit thescope or meaning of the claims.

Also, in the above Detailed Description, various features can be groupedtogether to streamline the disclosure. However, the claims cannot setforth every feature disclosed herein, as examples can feature a subsetof said features. Further, examples can include fewer features thanthose disclosed in a particular example. Thus, the following claims arehereby incorporated into the Detailed Description, with each claimstanding on its own as a separate example. The scope of the examplesdisclosed herein is to be determined with reference to the appendedclaims, along with the full scope of equivalents to which such claimsare entitled.

Example 1 is a system, comprising: hardware processing circuitry; one ormore hardware memories storing instructions that when executed configurethe hardware processing circuitry to perform operations comprising:receiving measurements of dynamic feature data for a geographic region;determining static features for the geographic region; generating apredicted background noise loudness in the geographic region during adefined time period using a model, the model trained on training dataincluding historical measurements of the dynamic feature data for aplurality of regions over a plurality of training time periods andstatic features of the plurality of regions, wherein the geographicregion is absent from the plurality of regions, wherein the defined timeperiod occurs after the plurality of training time periods.

In Example 2, the subject matter of Example 1 optionally includeswherein the generating of the predicted background noise loudnesspredicts the background noise loudness at a particular time of day and aparticular date within the defined time period, and the historicalmeasurements of the dynamic feature data are correlated with a time ofday and a date of the historical measurements.

In Example 3, the subject matter of any one or more of Examples 1-2optionally include the operations further comprising determining a routefor an aircraft based on the predicted background noise loudness in thegeographic region.

In Example 4, the subject matter of Example 3 optionally includes theoperations further comprising: predicting a first background noiseloudness in a first region during a time period based on the model;predicting a second background noise loudness in a second region duringthe time period based on the model; determining the first backgroundnoise loudness is higher than the second background noise loudness, androuting the aircraft through the first region during the time period inresponse to the determination.

In Example 5, the subject matter of any one or more of Examples 1-4optionally include the operations further comprising: generating, basedon a model and for each of a plurality of regions in a map, a predictedbackground noise loudness of the respective region; identifying anorigin and destination of an aircraft; identifying a plurality of routesfrom the origin to the destination, each of the plurality of routesincluding at least one of the plurality of regions in the map; comparingthe predicted background noise loudness of the at least one of theplurality of regions included in a first route of the plurality ofroutes to the predicted background noise loudness of the at least one ofthe plurality of regions included in a second route of the plurality ofroutes; selecting the first route or the second route based on thecomparison; and routing an aerial vehicle over the selected route.

In Example 6, the subject matter of Example 5 optionally includes theoperations further comprising: aggregating predicted background noiseloudness of regions included in the first route; aggregating predictedbackground noise loudness of regions included in the second route,wherein the selection of the first route or the second route is based onthe first aggregating and the second aggregating.

In Example 7, the subject matter of any one or more of Examples 5-6optionally include the operations further comprising: determining aminimum predicted background noise loudness along the selected route;comparing the minimum predicted background noise loudness to a noisethreshold; and determining an altitude for the aircraft along theselected route to be above a predefined altitude in response to theminimum predicted background noise loudness being below the noisethreshold.

Example 8 is a non-transitory computer readable storage mediumcomprising instructions that when executed configure hardware processingcircuitry to perform operations comprising: receiving measurements ofdynamic feature data for a geographic region; determining staticfeatures for the geographic region; generating a predicted backgroundnoise loudness in the geographic region during a defined time periodusing a model, the model trained on training data including historicalmeasurements of the dynamic feature data for a plurality of regions overa plurality of training time periods and static features of theplurality of regions, wherein the geographic region is absent from theplurality of regions, wherein the defined time period occurs after theplurality of training time periods.

In Example 9, the subject matter of Example 8 optionally includes theoperations further comprising determining a route for an aircraft basedon the predicted background noise loudness in the geographic region.

In Example 10, the subject matter of Example 9 optionally includes theoperations further comprising: predicting a first background noiseloudness in a first region during a time period based on the model;predicting a second background noise loudness in a second region duringthe time period based on the model; determining the first backgroundnoise loudness is higher than the second background noise loudness, androuting the aircraft through the first region during the time period inresponse to the determination.

In Example 11, the subject matter of any one or more of Examples 8-10optionally include the operations further comprising: generating, basedon a model and for each of a plurality of regions in a map, a predictedbackground noise loudness of the respective region; identifying anorigin and destination of an aircraft; identifying a plurality of routesfrom the origin to the destination, each of the plurality of routesincluding at least one of the plurality of regions in the map; comparingthe predicted background noise loudness of the at least one of theplurality of regions included in a first route of the plurality ofroutes to the predicted background noise loudness of the at least one ofthe plurality of regions included in a second route of the plurality ofroutes; selecting the first route or the second route based on thecomparison; and routing an aerial vehicle over the selected route.

In Example 12, the subject matter of Example 11 optionally includes theoperations further comprising: aggregating predicted background noiseloudness of regions included in the first route; aggregating predictedbackground noise loudness of regions included in the second route,wherein the selection of the first route or the second route is based onthe first aggregating and the second aggregating.

In Example 13, the subject matter of any one or more of Examples 11-12optionally include the operations further comprising: determining aminimum predicted background noise loudness along the selected route;comparing the minimum predicted background noise loudness to a noisethreshold; and determining an altitude for the aircraft along theselected route to be above a predefined altitude in response to theminimum predicted background noise loudness being below the noisethreshold.

Example 14 is a method performed by hardware processing circuitry,comprising: receiving measurements of dynamic feature data for ageographic region; determining static features for the geographicregion; generating a predicted background noise loudness in thegeographic region during a defined time period using a model, the modeltrained on training data including historical measurements of thedynamic feature data for a plurality of regions over a plurality oftraining time periods and static features of the plurality of regions,wherein the geographic region is absent from the plurality of regions,wherein the defined time period occurs after the plurality of trainingtime periods.

In Example 15, the subject matter of Example 14 optionally includeswherein the generating of the predicted background noise loudnesspredicts the background noise loudness at a particular time of day and aparticular date within the defined time period, and the historicalmeasurements of the dynamic feature data are correlated with a time ofday and a date of the historical measurements.

In Example 16, the subject matter of any one or more of Examples 14-15optionally include determining a route for an aircraft based on thepredicted background noise loudness in the geographic region.

In Example 17, the subject matter of Example 16 optionally includespredicting a first background noise loudness in a first region during atime period based on the model; predicting a second background noiseloudness in a second region during the time period based on the model;determining the first background noise loudness is higher than thesecond background noise loudness, and routing the aircraft through thefirst region during the time period in response to the determination.

In Example 18, the subject matter of any one or more of Examples 14-17optionally include generating, based on a model and for each of aplurality of regions in a map, a predicted background noise loudness ofthe respective region; identifying an origin and destination of anaircraft; identifying a plurality of routes from the origin to thedestination, each of the plurality of routes including at least one ofthe plurality of regions in the map; comparing the predicted backgroundnoise loudness of the at least one of the plurality of regions includedin a first route of the plurality of routes to the predicted backgroundnoise loudness of the at least one of the plurality of regions includedin a second route of the plurality of routes; selecting the first routeor the second route based on the comparison; and routing an aerialvehicle over the selected route.

In Example 19, the subject matter of Example 18 optionally includesaggregating predicted background noise loudness of regions included inthe first route; aggregating predicted background noise loudness ofregions included in the second route, wherein the selection of the firstroute or the second route is based on the first aggregating and thesecond aggregating.

In Example 20, the subject matter of any one or more of Examples 18-19optionally include determining a minimum predicted background noiseloudness along the selected route; comparing the minimum predictedbackground noise loudness to a noise threshold; and determining analtitude for the aircraft along the selected route to be above apredefined altitude in response to the minimum predicted backgroundnoise loudness being below the noise threshold.

We claim:
 1. A system, comprising: hardware processing circuitry; one ormore hardware memories storing instructions that when executed configurethe hardware processing circuitry to perform operations comprising:receiving measurements of dynamic feature data for a geographic region;determining static features for the geographic region; and generating apredicted background noise loudness in the geographic region during adefined time period using a model, the model trained on training dataincluding historical measurements of the dynamic feature data for aplurality of regions over a plurality of training time periods andstatic features of the plurality of regions, wherein the geographicregion is absent from the plurality of regions, wherein the defined timeperiod occurs after the plurality of training time periods.
 2. Thesystem of claim 1, wherein the generating of the predicted backgroundnoise loudness predicts the background noise loudness at a particulartime of day and a particular date within the defined time period, andthe historical measurements of the dynamic feature data are correlatedwith a time of day and a date of the historical measurements.
 3. Thesystem of claim 1, the operations further comprising determining a routefor an aircraft based on the predicted background noise loudness in thegeographic region.
 4. The system of claim 3, the operations furthercomprising: predicting a first background noise loudness in a firstregion during a time period based on the model; predicting a secondbackground noise loudness in a second region during the time periodbased on the model; determining the first background noise loudness ishigher than the second background noise loudness, and routing theaircraft through the first region during the time period in response tothe determination.
 5. The system of claim 1, the operations furthercomprising: generating, based on a model and for each of a plurality ofregions in a map, a predicted background noise loudness of therespective region; identifying an origin and destination of an aircraft;identifying a plurality of routes from the origin to the destination,each of the plurality of routes including at least one of the pluralityof regions in the map; comparing the predicted background noise loudnessof the at least one of the plurality of regions included in a firstroute of the plurality of routes to the predicted background noiseloudness of the at least one of the plurality of regions included in asecond route of the plurality of routes; selecting the first route orthe second route based on the comparison; and routing an aerial vehicleover the selected route.
 6. The system of claim 5, the operationsfurther comprising: aggregating predicted background noise loudness ofregions included in the first route; and aggregating predictedbackground noise loudness of regions included in the second route,wherein the selection of the first route or the second route is based onthe first aggregating and the second aggregating.
 7. The system of claim5, the operations further comprising: determining a minimum predictedbackground noise loudness along the selected route; comparing theminimum predicted background noise loudness to a noise threshold; anddetermining an altitude for the aircraft along the selected route to beabove a predefined altitude in response to the minimum predictedbackground noise loudness being below the noise threshold.
 8. Anon-transitory computer readable storage medium comprising instructionsthat when executed configure hardware processing circuitry to performoperations comprising: receiving measurements of dynamic feature datafor a geographic region; determining static features for the geographicregion; and generating a predicted background noise loudness in thegeographic region during a defined time period using a model, the modeltrained on training data including historical measurements of thedynamic feature data for a plurality of regions over a plurality oftraining time periods and static features of the plurality of regions,wherein the geographic region is absent from the plurality of regions,wherein the defined time period occurs after the plurality of trainingtime periods.
 9. The non-transitory computer readable storage medium ofclaim 8, the operations further comprising determining a route for anaircraft based on the predicted background noise loudness in thegeographic region.
 10. The non-transitory computer readable storagemedium of claim 9, the operations further comprising: predicting a firstbackground noise loudness in a first region during a time period basedon the model; predicting a second background noise loudness in a secondregion during the time period based on the model; determining the firstbackground noise loudness is higher than the second background noiseloudness, and routing the aircraft through the first region during thetime period in response to the determination.
 11. The non-transitorycomputer readable storage medium of claim 8, the operations furthercomprising: generating, based on a model and for each of a plurality ofregions in a map, a predicted background noise loudness of therespective region; identifying an origin and destination of an aircraft;identifying a plurality of routes from the origin to the destination,each of the plurality of routes including at least one of the pluralityof regions in the map; comparing the predicted background noise loudnessof the at least one of the plurality of regions included in a firstroute of the plurality of routes to the predicted background noiseloudness of the at least one of the plurality of regions included in asecond route of the plurality of routes; selecting the first route orthe second route based on the comparison; and routing an aerial vehicleover the selected route.
 12. The non-transitory computer readablestorage medium of claim 11, the operations further comprising:aggregating predicted background noise loudness of regions included inthe first route; and aggregating predicted background noise loudness ofregions included in the second route, wherein the selection of the firstroute or the second route is based on the first aggregating and thesecond aggregating.
 13. The non-transitory computer readable storagemedium of claim 11, the operations further comprising: determining aminimum predicted background noise loudness along the selected route;comparing the minimum predicted background noise loudness to a noisethreshold; and determining an altitude for the aircraft along theselected route to be above a predefined altitude in response to theminimum predicted background noise loudness being below the noisethreshold.
 14. A method performed by hardware processing circuitry,comprising: receiving measurements of dynamic feature data for ageographic region; determining static features for the geographicregion; and generating a predicted background noise loudness in thegeographic region during a defined time period using a model, the modeltrained on training data including historical measurements of thedynamic feature data for a plurality of regions over a plurality oftraining time periods and static features of the plurality of regions,wherein the geographic region is absent from the plurality of regions,wherein the defined time period occurs after the plurality of trainingtime periods.
 15. The method of claim 14, wherein the generating of thepredicted background noise loudness predicts the background noiseloudness at a particular time of day and a particular date within thedefined time period, and the historical measurements of the dynamicfeature data are correlated with a time of day and a date of thehistorical measurements.
 16. The method of claim 14, further comprisingdetermining a route for an aircraft based on the predicted backgroundnoise loudness in the geographic region.
 17. The method of claim 16,further comprising: predicting a first background noise loudness in afirst region during a time period based on the model; predicting asecond background noise loudness in a second region during the timeperiod based on the model; determining the first background noiseloudness is higher than the second background noise loudness, androuting the aircraft through the first region during the time period inresponse to the determination.
 18. The method of claim 14, furthercomprising: generating, based on a model and for each of a plurality ofregions in a map, a predicted background noise loudness of therespective region; identifying an origin and destination of an aircraft;identifying a plurality of routes from the origin to the destination,each of the plurality of routes including at least one of the pluralityof regions in the map; comparing the predicted background noise loudnessof the at least one of the plurality of regions included in a firstroute of the plurality of routes to the predicted background noiseloudness of the at least one of the plurality of regions included in asecond route of the plurality of routes; selecting the first route orthe second route based on the comparison; and routing an aerial vehicleover the selected route.
 19. The method of claim 18, further comprising:aggregating predicted background noise loudness of regions included inthe first route; and aggregating predicted background noise loudness ofregions included in the second route, wherein the selection of the firstroute or the second route is based on the first aggregating and thesecond aggregating.
 20. The method of claim 18, further comprising:determining a minimum predicted background noise loudness along theselected route; comparing the minimum predicted background noiseloudness to a noise threshold; and determining an altitude for theaircraft along the selected route to be above a predefined altitude inresponse to the minimum predicted background noise loudness being belowthe noise threshold.